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OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization. | LitMetric

AI Article Synopsis

  • OHDSI (Observational Health Data Sciences and Informatics) is a massive distributed data network with over 331 sources and 2.1 billion patient records, facilitating large-scale observational research through standardized data.
  • The OHDSI Standardized Vocabularies, a crucial component of this network, include more than 10 million concepts from 136 vocabularies, allowing for better data harmonization and easier research execution.
  • This open-source vocabulary system addresses challenges in observational research, enabling various analyses such as efficient phenotyping and patient-level predictions, and encourages researchers to utilize and contribute to its ongoing development.

Article Abstract

Importance: The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization.

Materials And Methods: We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts.

Results: The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting.

Discussion And Conclusion: OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10873827PMC
http://dx.doi.org/10.1093/jamia/ocad247DOI Listing

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